Analysis

Data analysis was initiated during the first PDSA cycle of the project and has been consistently executed every week until the project concluded (Appendix D). Descriptive[A1] analysis is employed to conduct a detailed analysis of each outcome measure.

Increased Prediabetes ScreeningRate[A2]

This outcome measure employed nominal data to determine the percentage of patients screened by providers for prediabetes throughout the implementation period. The data calculations for this outcome measure involved taking the total number of patients screened using the CDC prediabetes risk assessment tool during the implementation period, dividing that number by the total annual patient visits, and multiplying that result by 100 to derive a percentage rate.

During the implementation period, there were 62 routine annual patient visits to the clinic, with providers screening 51 patients (Figure 1), leading to a prediabetes screening rate of 82%. Overall, there was a significant increase in the percentage of patients screened post-intervention compared to pre-intervention, as measured by dividing the number of screening opportunities (n=62) by the actual number of patients screened (n=51), resulting in an 82% screening rate (Figure 2). The project successfully achieved its goal of a minimum 50% increase in patients screened for prediabetes post-intervention compared to baseline screening rates of 20% (Figure 3).

[A1]What type of statistical program did you use for data analysis? SAS or SPSS, for example?

[A2]Data analysis needed to be more extensive than the information you provided. Were you able to show a correlation between various factors that could be affecting the screening process?

Question 1: What statistical software did you use and how was the data analyzed

Question 2: Data analysis needed to be more extensive than the information you provided. Were you able to show a correlation between various factors that could be affecting the screening process?

How do I answer question 2 and analyze ?